Glass Frog Calls

<p>This dataset contains 5296 audio files in .WAV format, corresponding to calls of two glass frog species:&nbsp;<em>Hyalinobatrachium fleischmanni</em> (Hf) and <em>Espadarana prosoblepon </em>(Ep), recorded under controlled laboratory conditions. We further increased the dataset size using noise injection-based data augmentation in one species (Ep), and artificially shifting call frequency in both species. The dataset contains four labelled classes: Hf (1250 calls), Hf-shifted (1250 calls), Ep (1398 calls) and Ep-shifted (1398 calls); each class is in a separate folder.</p> <p>Noise injected files, with white and pink noise, were generated using noise factors of 0.010 and 0.020, respectively, using the random and powerlaw_psd_gaussian functions from numpy (v1.26.4) and colorednoise (v2.2.0) Python libraries.</p> <p>Frequency shifting placed calls in upper or lower parts of spectrum, incorporating additional variability and was used to create two new classes. Hf call frequency was increased by a factor of +4 semitones (4/12), while Ep call frequency was decreased by the same factor (-4), using librosa (v0.10.2) in Python (effects.pitch_shift). Modified calls (&lsquo;Hf-shifted&rsquo; and &lsquo;Ep-shifted&rsquo;, respectively) more closely resemble the calls of the other species in terms of their frequency band.</p> <p>Derived audio files partially share file names: '1104-18211-ROI1_pN.WAV' is the 'pink noise' version of '1104-18211-ROI1.WAV', etc.</p> <p>The audio collection is accompanied by a data table (.csv) with no missing or null values, and consists of 4 columns: &ldquo;File_name&rdquo;; &ldquo;Data_Augmentation&rdquo;, whether the file had noise injection or not, and its type (white or pink); &ldquo;Frequency_Shift&rdquo;, whether the audio frequency was artificially shifted or not; and &ldquo;Class&rdquo;, which includes the respective labels. The dataset is suitable for machine learning tasks, audio signal processing and statistical analysis.</p>